Inference and learning systems for uncertain relational data / Giuseppe Cota.

Includes bibliographical references.

Online resource; title from PDF title page (IOS Press, viewed August 24, 2018).

Intro; Title Page; Abstract; Acknowledgements; Contents; List of Figures; List of Tables; List of Algorithms; List of Acronyms; Introduction; Motivation; Aims of the Thesis; Structure of the Thesis; Structure; Thesis Contributions; Inference in Probabilistic Logic Programming; Inference in Probabilistic Description Logics; Learning Systems in Probabilistic Logic Programming; Learning Systems in Probabilistic Description Logics; How to read this thesis; Probabilistic Logics; Fundamentals of First-Order Logic and Logic Programming; Introduction; First-Order Logic; Syntax; Tarski's semantics.

Logic ProgrammingProlog; Normal Logic Programs; First-Order Logic vs Logic Programs; Conclusions; Distribution Semantics; Introduction; Formal Definition; Conclusions; Probabilistic Logic Programming Languages; Introduction; Logic Programs with Annotated Disjunctions; LPADs Syntax; LPADs Semantics; ProbLog; ProbLog Syntax; Conclusions; Description Logics and OWL; Introduction; Description Logics; Syntax; Concept and Role Constructors; Concept Constructors; Role constructors; Knowledge Base; Nomenclature; Semantics; Decidability of Description Logics; Description Logics and First-Order Logic.

The OWL Ontology LanguageOWL Syntax; OWL sublanguages; Tools for OWL; Conclusions; Reasoning in Description Logics; Reasoning Problems; Closed vs Open World Assumption; Reasoning Techniques; Pellet; Tableau Algorithm; Explanation finding; Pinpointing formula; Conclusions; Probabilistic Description Logics; Introduction; The Distribution Semantics for Description Logics: DISPONTE; Syntax; Semantics; Assumption of Independence; Related Work; Conclusions; Inference in Probabilistic Logics; Decision Diagrams; Introduction; Multivalued Decision Diagrams; Binary Decision Diagrams; Conclusions.

Fundamentals of Exact Probabilistic Logical InferenceInference Approaches; Exact Probabilistic Logical Inference; Splitting Algorithm; Inference with Multi-valued Decision Diagrams; Inference with Binary Decision Diagrams; Conclusions; Inference in Probabilistic Logic Programming; Introduction; cplint; Exact Inference: the PITA module; Approximate Inference: the MCINTYRE module; Causal Inference with cplint; Causal Inference in PLP; Causal Exact Inference with cplint; Causal Approximate Inference with cplint; Notable Examples; Simpson's Paradox; Viral Marketing; Experiments.

Hybrid Probabilistic Logic Programs with cplintSampling the Arguments of Unconditional Queries over Hybrid Programs; Conditional Queries over Hybrid Logic Programs; cplint on SWISH: a Web interface for cplint; SWISH; cplint on SWISH; Examples; Related Work; Work on causality inference; Work on Hybrid Probabilistic Logic Programs; Web application for PLP; Conclusions; Inference in Probabilistic Description Logics; Introduction; BUNDLE; How to use BUNDLE; TRILL; TRILLP; How to use TRILL and TRILLP; TRILL on SWISH; Inference Complexity; Experiments; Comparing the Systems; Related Work.

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